
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


lowerCamelCase_    :			Optional[int]    =	logging.get_logger(__name__)

lowerCamelCase_    :			Optional[int]    =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_    :			List[str]    =	{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_    :			str    =	{
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Union[str, Any]          =			VOCAB_FILES_NAMES
			__UpperCamelCase   :    str          =			PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase   :    List[str]          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase   :    List[Any]          =			["""input_ids""", """attention_mask"""]
			__UpperCamelCase   :    List[str]          =			GPTaTokenizer

			def __init__(							self			:							str    ,						snake_case_			:							Optional[int]=None    ,						snake_case_			:							List[Any]=None    ,						snake_case_			:							Tuple=None    ,						snake_case_			:							List[Any]="<|endoftext|>"    ,						snake_case_			:							Union[str, Any]="<|endoftext|>"    ,						snake_case_			:							Optional[Any]="<|endoftext|>"    ,						snake_case_			:							List[str]=False    ,						**snake_case_			:							Optional[int]    ,						):
							super().__init__(
							    snake_case_    ,						snake_case_    ,						tokenizer_file=snake_case_    ,						unk_token=snake_case_    ,						bos_token=snake_case_    ,						eos_token=snake_case_    ,						add_prefix_space=snake_case_    ,						**snake_case_    ,						)

							UpperCamelCase_:				str						=    kwargs.pop("""add_bos_token"""    ,						snake_case_   )

							UpperCamelCase_:				Optional[int]						=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
							if pre_tok_state.get("""add_prefix_space"""    ,						snake_case_   ) != add_prefix_space:
											UpperCamelCase_:				int						=    getattr(snake_case_    ,						pre_tok_state.pop("""type"""   )   )
											UpperCamelCase_:				Tuple						=    add_prefix_space
											UpperCamelCase_:				List[Any]						=    pre_tok_class(**snake_case_   )

							UpperCamelCase_:				List[str]						=    add_prefix_space

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						*snake_case_			:							Optional[Any]    ,						**snake_case_			:							int   ):
							UpperCamelCase_:				str						=    kwargs.get("""is_split_into_words"""    ,						snake_case_   )
							assert self.add_prefix_space or not is_split_into_words, (
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*snake_case_    ,						**snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						*snake_case_			:							Tuple    ,						**snake_case_			:							Optional[Any]   ):
							UpperCamelCase_:				int						=    kwargs.get("""is_split_into_words"""    ,						snake_case_   )

							assert self.add_prefix_space or not is_split_into_words, (
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*snake_case_    ,						**snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							str    ,						snake_case_			:							Optional[str] = None   ):
							UpperCamelCase_:				int						=    self._tokenizer.model.save(snake_case_    ,						name=snake_case_   )
							return tuple(snake_case_   )




			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							"Conversation"   ):
							UpperCamelCase_:				Dict						=    []
							for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(snake_case_    ,						add_special_tokens=snake_case_   ) + [self.eos_token_id]   )

							if len(snake_case_   ) > self.model_max_length:
											UpperCamelCase_:				Any						=    input_ids[-self.model_max_length :]
							return input_ids








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch

							from transformers.generation import DisjunctiveConstraint

@require_torch
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							Dict   ):
							# For consistency across different places the DisjunctiveConstraint is called,
							# dc.token_ids is a list of integers. It is also initialized only by integers.

							UpperCamelCase_:				Optional[int]						=    [[1, 2, 4], [1, 2, 3, 4]]
							UpperCamelCase_:				List[str]						=    DisjunctiveConstraint(snake_case_   )
							self.assertTrue(isinstance(dc.token_ids    ,						snake_case_   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							# We can't have constraints that are complete subsets of another. This leads to a preverse
							# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
							# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
							# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
							# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
							UpperCamelCase_:				Dict						=    [[1, 2], [1, 2, 3, 4]]

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(snake_case_   )  # fails here

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Dict						=    [[1, 2, 3], [1, 2, 4]]

							UpperCamelCase_:				Dict						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    dc.update(1   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(2   )
							UpperCamelCase_:				Tuple						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(3   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is True and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 3]   )




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

							UpperCamelCase_:				Tuple						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Dict						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Any						=    dc.update(4   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2, 4]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

							dc.reset()

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 3   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Tuple						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 2   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[str]						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.remaining() == 0   )
							self.assertTrue(dc.current_seq == [1, 2, 5]   )






